Study of Intra- and Inter-user Variance in Password Keystroke Dynamics

Blaine Ayotte

, Mahesh K. Banavar

, Daqing Hou

and Stephanie Schuckers

Department of Electrical and Computer Engineering, Clarkson University, 8 Clarkson Avenue, Potsdam, U.S.A.

Keywords:

Behavioral Biometrics, Keystroke Dynamics, Principle Component Analysis, User Authentication.

Abstract:

Keystroke dynamics study how users input text via their keyboards. Having the ability to differentiate users,

typing behaviors can unobtrusively form a component of a behavioral biometric recognition system to improve

existing account security. However, because keystroke dynamics is behavioral biometric typing patterns can

change over time. The temporal effects of keystroke dynamics are largely unstudied beyond empirically

demonstrating that error rates will be higher for old or outdated proﬁles. In this paper, the effects on typing

patterns over time is investigated in detail. Using a well-known ﬁxed-text keystroke dynamics dataset, we

show overall typing time for a provided password “.tie5Roanl” changes signiﬁcantly over time, decreasing by

almost 30%. Principal component analysis (PCA) is used to determine which monographs and digraphs tend

to change throughout time. Rarely typed features, such as digraphs with a letter and number, are most likely

to change over time, while commonly occurring features such as common digraphs and monographs are much

more stable.

1 INTRODUCTION

Keystroke dynamics is a behavioral biometric that uti-

lizes typing rhythms to determine user identity (Alsul-

tan and Warwick, 2013; Banerjee and Woodard, 2012;

Teh et al., 2013). Keystroke dynamics can be used to

provide an additional layer of security to traditional

single sign-on authentication systems by also requir-

ing the typing patterns to match the proﬁle of an indi-

vidual. Furthermore, as most computers already have

a keyboard, this layer of security does not require any

additional hardware.

There are two main types of keystroke dynamics:

ﬁxed-text and free-text. Fixed-text analysis requires

the keystrokes of the proﬁle and test sample to be

identical. The ﬁxed-text keystrokes can constitute a

password, phrase, or even a sequence of sentences. A

common application of ﬁxed-text keystroke dynam-

ics is password hardening, where typing patterns are

used to further secure a password so that users need

not only the password to match but also the keystroke

timings as well. Free-text keystroke dynamics puts

no restrictions on the keystrokes that users can type.

Common applications are continuous user authenti-

https://orcid.org/0000-0003-1909-4951

https://orcid.org/0000-0002-3916-7137

https://orcid.org/0000-0001-8401-7157

https://orcid.org/0000-0002-9365-9642

cation and it typically requires more keystrokes to

achieve similar performance to ﬁxed-text. For ﬁxed-

text systems, the keystrokes input are identical each

time making investigations more straightforward.

Features commonly used in keystroke dynam-

ics are combinations of monographs and digraphs

(Gunetti and Picardi, 2005; Huang et al., 2017; Mon-

rose and Rubin, 1997; Teh et al., 2013). Monographs

are deﬁned as the hold time of a key and digraphs

are deﬁned as the elapsed time between two consec-

utive keypresses, respectively. A graphical represen-

tation of sequentially pressing and releasing the “e”

and “5” keys with the corresponding monographs and

digraphs is shown in Figure 1.

Figure 1: Graphical representation of how monograph and

the four different digraph features are produced from two

consecutive keystrokes. U and D correspond to key-up and

key-down and are used to deﬁne the monographs and di-

graphs.

A survey of over 180 keystroke dynamics papers

(Teh et al., 2013), found that 49% used digraphs, 41%

Ayotte, B., Banavar, M., Hou, D. and Schuckers, S.

Study of Intra- and Inter-user Variance in Password Keystroke Dynamics.

DOI: 10.5220/0010235504670474

In Proceedings of the 7th International Conference on Information Systems Security and Privacy (ICISSP 2021), pages 467-474

ISBN: 978-989-758-491-6

467

used monographs, 5% used pressure, and 5% used

other features. The survey points out that research in-

vestigating and comparing common features used for

keystroke dynamics is missing. Furthermore, there is

no work that investigates which features, if any, are

likely to change over time.

Keystroke dynamics systems consist of two steps,

training and testing. Keystrokes from an authorized

user are collected and stored in a proﬁle. During the

testing phase, keystrokes from an unknown user are

compared to the previously recorded proﬁle. If the

proﬁle and test keystrokes are similar enough the un-

known user is authenticated and allowed access.

Most behavioral biometrics generally experience

lower permanency compared to physiological biomet-

rics (Teh et al., 2013). Keystroke dynamics is a be-

havioral biometric and is no exception. Typing pat-

terns of users may gradually change due to some or all

of the following factors: familiarity towards a pass-

word, maturing typing proﬁciency, adaptation to in-

put devices, and other environmental factors. While

researchers have methods to circumvent this issue by

constantly updating stored keystroke proﬁle, there is

a lack of study on how keystroke patterns are affected

throughout time.

In this paper, we investigate the effects of time on

keystroke typing patterns. Both features that are sta-

ble and change throughout time are determined using

principal component analysis (PCA). This work al-

lows researchers to better understand and model how

typing patterns change over time. This paper is orga-

nized as follows. Section 2 presents related work. In

Section 3 the CMU password ﬁxed-text dataset is de-

scribed. Section 4 demonstrates how typing patterns

change over time. In Section 5 principal component

analysis (PCA) is performed on the keystroke data.

Lastly, Section 6 concludes the paper.

2 RELATED WORK

It was found by Giot, et al., that the performance

of keystroke dynamics systems decreased across ses-

sions for multiple publicly available datasets (Giot

et al., 2015). Using the ﬁrst session as training and

the subsequent sessions as testing, Giot found the

equal error rate (EER) increased and area under the

ROC curve (AUC) decreased. This was attributed to a

change in typing patterns over the course of data col-

lection. Keystroke dynamics is a behavioral biometric

and therefore user behavior is expected to change over

time, but how the typing patterns evolve over time has

not been studied.

Ngugi and Kahn compared typing patterns of a

four-digit PIN “1234” over three separate sessions

with a week and then a month in between the ses-

sions (Ngugi et al., 2011). The pins were solicited

during a “world trivia quiz” where users answered

questions by typing T/F (true or false) and entered

their pins. Over time the false accept rate (FAR) did

increase slightly, however, the false reject rate (FRR)

increased signiﬁcantly. The increase was largest after

one week and after another month the increase was

less severe. The only change in typing behavior ob-

served was an overall typing speed increase over time.

As users became more familiar with the PIN, their av-

erage typing speeds increased. However, further anal-

ysis was not done to see which parts of typing the PIN,

if any, changed more than others.

Gunetti, et al., conducted a longitudinal study of

users typing behaviors across 1.5 years (Gunetti et al.,

2005). The 30 native Italian speaking participants

contributed free-text samples in both Italian and En-

glish. It was found that users could still be authenti-

cated using their old proﬁles with FAR of 1.84% and

FRR of 6.67% (in Italian). From the few studies ex-

amining proﬁle age and performance, it is clear the

that performance will be impacted especially in the

FRR. Intuitively, this makes sense as imposter behav-

ior is not likely to change to be closer to your behavior

than to change farther from your behavior. However,

your behavior is likely to deviate from your proﬁle

causing an increase in the FRR.

To mitigate the effects of typing patterns changing

over time, Kang, et al., used moving windows and

growing windows for proﬁle retraining (Kang et al.,

2007). For the moving window scenario, the number

of training patterns is ﬁxed, and when a new pattern

is added, the oldest is removed. Growing windows

continuously add new patterns without removing the

older ones. Kang, et al., found that using moving and

growing windows both decreased the EER, although

they concluded that more work needs to be done as

the typing patterns were collected over a short period

of time.

While the full extent of proﬁling aging is un-

known, many researchers have developed updating

strategies, or retraining schemes, to mitigate temporal

effects (Giot et al., 2011; Gunetti et al., 2005; Ngugi

et al., 2011; C¸ eker and Upadhyaya, 2017). These tem-

plate updating strategies, while effective, are useless

if the only keystrokes available are old or outdated.

Research is required to better understand how typing

patterns evolve over time to develop robust strategies

to mitigate template aging when working with out-

dated proﬁles.

ICISSP 2021 - 7th International Conference on Information Systems Security and Privacy

468

In summary, keystroke dynamics is a behavioral

biometric, and typing patterns can change over time.

Current research in keystroke dynamics focuses on

strategies to update proﬁles and further improve per-

manence empirically. Our focus is on understanding

and predicting how behavior changes so that more ef-

fective modeling can be done.

3 DATASET

In this work, the CMU ﬁxed-text dataset is used (Kil-

lourhy and Maxion, 2009). This dataset was collected

to study password hardening and consists of 51 users,

each with 400 total password entries across 8 different

sessions. Each session contains 50 password attempts

and there is at least one day between each session. All

users were required to type “.tie5Roanl” without any

errors. The password was provided to users so they

may not be initially familiar with typing the password.

The dataset consists of 31 features including mono-

graphs, DD digraphs, and UD digraphs. Monographs

are deﬁned as the hold time of a key, DD digraphs are

the elapsed time between the key-down of a key to the

key-down of the following key, and UD digraphs are

the time of a key released to the press of the follow-

ing key (see Figure 1). The CMU dataset is one of

the largest publicly available ﬁxed-text datasets and is

used by many different researchers. As a result, this

dataset serves as a benchmark dataset for ﬁxed-text

research and is well-known in the keystroke dynam-

ics ﬁeld.

4 KEYSTROKE DYNAMICS

OVER TIME

It has been empirically well established that behav-

ioral biometrics can change over time (Giot et al.,

2011; Giot et al., 2015; Gunetti et al., 2005; Kang

et al., 2007; Mhenni et al., 2019; Ngugi et al., 2011).

These works have shown on multiple datasets that the

EER increases over time without any template updat-

ing strategy. This phenomenon can be seen in Fig-

ure 2 where the average EER is shown for each of

the sessions. The algorithm used for authentication

is the scaled Manhattan distance, previously found to

be the best performing algorithm on this dataset (Kil-

lourhy and Maxion, 2009). The EER is calculated

using 31 features consisting of monographs, DD di-

graphs, and UD digraphs extracted from the password

“.tie5Roanl”, similar to the work done by Kilhourhy

and Maxion (Killourhy and Maxion, 2009).

Figure 2: EER versus session averaged across user using the

scaled Manhattan distance (Killourhy and Maxion, 2009).

When no update strategy is used, the EER is increasing

steadily as the testing keystrokes get farther away from the

proﬁle. When the proﬁle is updated after each session, the

EER does not increase.

Figure 3: Total time in seconds taken to type the password

“.tie5Roanl” averaged across session and users.

When no template updating strategy is deployed,

session 1 is used as the proﬁle and tested against the

keystrokes in sessions 2-8. For our template updating

strategy, we consider a simple method where the pro-

ﬁle is updated after each session. The test keystrokes

are always from the session immediately following

the proﬁle.

To investigate how typing patterns change for

users typing the password “.tie5Roanl” overall typ-

ing time is examined. The password is provided to

the users and therefore is unfamiliar. Prior to the ﬁrst

session they have had no time to practice. The pass-

word contains uncommon combinations of keystrokes

(number to letter and letter to number such as “e+5”

and “5+R”) and as a result users may struggle at ﬁrst

to enter it quickly. Therefore, we expect the total time

Study of Intra- and Inter-user Variance in Password Keystroke Dynamics

469

in the ﬁrst session to be higher than in the sessions

following. This phenomenon can be seen in Figure 3.

Figure 3 is consistent with our intuition. On aver-

age, users type the password faster than the session

previously. Our ﬁndings are consistent with previ-

ous work that found the overall typing time decreased

across session (Ngugi et al., 2011). From sessions 1

to 8, the average typing time drops by almost one sec-

ond which is a percent decrease of 29.8%. Therefore,

it is no surprise that without updating the proﬁle after

each session the EER will increase signiﬁcantly.

While this difference in behavior is easy to un-

derstand, which features (the individual monographs

and digraphs) are changing is unknown. To better un-

derstand the changing behavior and to determine if

only certain features are changing, principal compo-

nent analysis (PCA) is applied to the keystroke data.

5 PRINCIPAL COMPONENT

ANALYSIS

Principal component analysis (PCA) is a common

tool used for dimensionality reduction or noise reduc-

tion (Abdi and Williams, 2010; Duda et al., 2012).

PCA works to project the data into a lower dimen-

sional space that best represents the data in terms of

variance in a least-square sense. The principal com-

ponents act as linear transformations from the original

data space into a new space (lower dimensionality)

where the majority of the total variance in the original

dataset is still explained. The components are sorted

in order of the percentage of variance in the original

dataset they explain.

Another common application of PCA is noise re-

duction. Consider a simple 2-dimensional example

where the data is produced with function f (x) = x+ε,

where ε is additive white Gaussian noise with a small

variance. Performing PCA on this data will return one

component which explains almost all of the variance.

This component corresponds to the deterministic sig-

nal f (x) = x. The other components explain the re-

mainder of the variance and correspond to the noise in

system. In this example, keeping only the ﬁrst compo-

nent will reduce noise in the system as well as reduce

the dimension of the data.

For keystroke dynamics, by cleverly selecting the

data to which PCA is applied, insights into how typing

behavior changes over time can be obtained. We con-

sider two scenarios for further analysis which we call,

“intra-user” and “inter-user”. Intra-user applies PCA

on a single user’s data to determine how each user’s

behavior changes over time, while inter-user applies

PCA on data from every user to determine which fea-

tures distinguish between users.

5.1 Intra-user PCA

For intra-user PCA, PCA is performed on the typ-

ing data from all sessions for each user. As we have

seen in Figure 3, the total time is changing signiﬁ-

cantly across sessions. Therefore, in this scenario, the

largest source of variability within the data is from

how the keystroke dynamics are changing over time

for a particular user. The principal components that

explain the majority of the variance in the original

dataset are linear combinations of features that are

causing the most change in typing patterns over time.

The remaining components can be thought of as noise

caused by human error, timing resolution in keystroke

capturing software, time of day, the mood of the typ-

ist, and any other factors leading to different password

entries.

After PCA is applied on the original 31 dimen-

sional data consisting of monograph and digraph du-

rations, we need to determine how many components

should be kept. To keep 100% of the total variance in

the original data, all 31 components will need to be

kept. Usually the ﬁrst few components will contain

enough of the total variance and a signiﬁcant number

of components can be discarded. A common method

of selecting the number of components to keep is re-

ferred to as a scree or elbow plot (Abdi and Williams,

2010). The idea behind a scree plot is to stop using

components after their contribution to the explained

variance begins to decline. In the intra-user PCA case,

for the majority of users, after the third component

the gain from from adding additional components de-

creased. This can be visualized in Figure 4, where the

scree plots for four different users are shown.

In Table 1, the explained variance for the ﬁrst

three components is shown for intra-user PCA. The

explained variance is averaged across users and the

standard deviation is also reported. To better under-

stand which original features (monographs and di-

graphs) are affected by time we take in depth look at

the principal components of the PCA decomposition.

Table 1: The average and standard deviations of the vari-

ance for the ﬁrst 3 components of the intra-user PCA de-

compositions.

Component Average Explained Variance (%)

1 37.7 ± 8.4

2 17.6 ± 3.4

3 11.9 ± 2.5

Sum 67.2 ± 7.1

ICISSP 2021 - 7th International Conference on Information Systems Security and Privacy

470

Figure 4: Scree plots for the intra-user PCA for four differ-

ent users. After the third component, there is minimal gain

(less than 5%) from adding additional components indicat-

ing only the ﬁrst three components should be kept.

It is worth noting that for every user’s PCA de-

composition, the explained variance and principal

components obtained are different. These compo-

nents are not typically compared because they can

represent completely different linear combinations of

original features. For example, each user may have a

very different percentage of variance explained in the

ﬁrst component from the other users. However, be-

cause the components look similar for most users, we

averaged the explained variances together in order to

best demonstrate global trends.

The original features consist of 31 monographs

and digraphs (seen in the ﬁrst column of Figure 5).

The principal components are linear combinations of

these features. Components with a large magnitude

weight for a particular feature indicates that feature

is contributing strongly to explain the variance of the

dataset. Therefore, that feature is considered to be

strongly inﬂuenced by time. In other words, if a fea-

ture weight has a large magnitude from the principal

component, then that feature is changing from session

to session.

The ﬁrst principal component is shown in Fig-

ure 5. On average (across users) the ﬁrst compo-

nent explains 37.7% of the total variance in the data.

Each weight of the component is squared because

we are interested in the magnitude of the weights.

The principal component corresponds to a direction,

and because each element of the component has been

squared, the sum of every column is equal to 1.

For almost every user the DD and UD digraphs

for “e+5” has the largest weight followed closely by

the “5+R” UD and DD digraphs. While only 20 users

are shown in Figure 5, this trend can be seen across

most users. Other features that seem to contribute for

most users but with much smaller weights include DD

and UD digraphs of “l+Return”, “.+t”, and “i+e”. The

DD and UD digraph contributions seem to be almost

identical regardless if the digraph is a strong or weak

contributor. Another interesting trend is that mono-

graphs do not appear to contribute whatsoever.

The second and third components explain on av-

erage 17.6% and 11.9% of the total variance respec-

tively. These components are not shown in order to

save space, and instead we present the general trends.

Unlike the ﬁrst component, the second and third com-

ponents have less consistent patterns across users sug-

gesting that the important features in these compo-

nents vary from user to user.

However, it is clear that some digraphs contribute

more than others. The stronger DD and UD digraph

contributors are shown in approximate ranked order

for the top 3 components: “e+5”, “5+R”, “l+Return”,

“.+t”, and “i+e”. These features are the least sta-

ble features across time. No monograph feature con-

tributes to a component within the ﬁrst three compo-

nents. These digraphs with a letter and number com-

bination are not commonly typed in everyday life so

users likely have little to no experience typing them.

The other digraphs shown to change over time include

a period and Return key (also less common in every-

day life).

The digraphs that are rarely typed tend to change

across sessions while the commonly typed digraphs

do not. In this dataset, the password “.tie5Roanl” was

provided for the participants, so users are not familiar

with the key combinations. It is apparent that famil-

iarity is a signiﬁcant factor when considering typing

patterns over time. The one exception to this rule ap-

pears to be the DD and UD digraphs of “i+e” as they

are commonly typed digraphs yet still seem to ﬂuc-

tuate over time. A possible explanation is that the

“i+e” digraph occurs nearby the “5” key. Nonethe-

less, familiarity with certain digraphs directly affects

whether that digraph is likely to change over time.

Monographs contribute very little to the princi-

pal components until about the 7

component. It is

not until the 11

component that contribution from

monographs becomes common. The 7

component,

on average, explains 4.0% of the variance and the ﬁrst

six components explain 87.8% of the total variance in

the data. This provides evidence that monographs are

mostly static over time and do not change as much as

digraphs. Considering only the English alphabet there

are 26 different monographs but 26 × 26 digraphs.

Therefore, each monograph may naturally get more

practice due to a much smaller amount of possibil-

ities, which reinforces that familiarity is driving the

change in behavior.

Study of Intra- and Inter-user Variance in Password Keystroke Dynamics

471

Figure 5: The ﬁrst principal component of the intra-user PCA decomposition. For space, only the ﬁrst 20 users components

are shown. Each PCA weight is squared so the columns sum to 1. Larger values appear in red and smaller values appear in

green. The DD and UD digraphs contribute roughly the same for a given letter combination and the two largest contributors

are “e+5” and “5+R”.

Existing work by Ayotte, et al, (Ayotte et al.,

2019; Ayotte et al., 2020) found that with small

amounts of keystrokes monographs were the among

the most informative features for distinguishing be-

tween users. A password hardening scenario involves

only small amounts of keystrokes so it is likely that

monographs are useful for distinguishing between

users. This work shows that monographs seem static

across time and therefore may be a powerful feature

for user authentication especially when working with

an old or outdated proﬁle.

5.2 Inter-user PCA

The inter-user case is designed to determine which

features are useful to distinguish between users. To

achieve this, the monographs and digraphs are aver-

aged across every session for each user. If the fea-

tures are not averaged, the results might depend on

the particular session used. PCA is then applied on

the single averaged features for each user. In this sce-

nario the largest source of variability within the data

is the different users. Therefore, the principal compo-

nents that can explain the majority of the variance in

the data are linear combinations of features that dif-

ferentiate users. The remaining components can be

thought of as noise caused by averaging across mul-

tiple sessions in addition to the sources explained in

Section 5.1.

The percentage explained variance for the inter-

user PCA for the top 3 components is 70.7%, 9.4%,

and 5.3% for a total of 85.4%. As the ﬁrst compo-

nent explains a signiﬁcant portion of the variance,

no scree plots are needed and it is adequate to only

consider the ﬁrst component. Nonetheless, Figure 6

contains the ﬁrst 3 principal components for the inter-

user PCA. The top features primarily contributing to

the ﬁrst component include “5+R”, “l+Return”, and

“.+t” followed closely by “e+5”, “R+o”, and “n+l”.

As with the intra-user PCA, the DD and UD digraphs

contribute roughly the same for a given letter combi-

nation.

Many of these features are the same as for the

intra-class PCA. This is consistent with empirical

EERs increasing over time. If the features that explain

differences between users were different from the fea-

tures that changed over time, the EERs would likely

not have changed over time. Therefore, digraphs such

as “R+o” and “n+l”, which contribute to the inter-

class PCA but not the intra-class PCA, may be espe-

cially more useful when working with old or outdated

proﬁles.

Monographs start to become contributors to the

principal components as early as the third component.

The contributions are smaller than the digraphs. How-

ever, the monograph feature was also shown to be sta-

ble over time. This is consistent with previous work

that found with small amounts of keystrokes mono-

graphs were informative features for distinguishing

ICISSP 2021 - 7th International Conference on Information Systems Security and Privacy

472

Figure 6: The ﬁrst 3 principal components of the inter-user

PCA decomposition. Each PCA weight is squared. Larger

values appear in red and smaller values appear in green.

between users (Ayotte et al., 2019; Ayotte et al.,

2020). Therefore, especially for old or outdated pro-

ﬁles, monographs may be a valuable feature for au-

thentication.

6 CONCLUSION AND FUTURE

WORK

In this paper, we investigated the temporal effects on

user typing patterns using a well-known ﬁxed-text

keystroke dynamics dataset. We showed that when

provided with an unfamiliar password (“.tie5Roanl”),

the overall typing time decreases throughout sessions.

Through intra-user PCA, we demonstrated the UD

and DD digraphs rarely occurring in normal typed

texts such as “e+5”, “5+R”, “l+Return” and “.+t” con-

tributed signiﬁcantly to the principal components that

explain the majority of variance in the original data,

and therefore subject to change over time. The UD

and DD digraphs that were commonly typed such as

“o+a” and “a+n” contributed almost nothing to the

explained variance and therefore found to be invari-

ant across time. Monographs were found to explain

almost none of the variance for the intra-user PCA,

which indicates they are stable over time. Mono-

graphs, in general, are typed far more frequently than

digraphs due to the smaller number of total possible

combinations.

This provides evidence that familiarity will

greatly impact how much a feature changes over time.

Features typed frequently such as monographs and

common digraphs are more invariant through time

than rarely typed features such as number-letter or let-

ter number digraphs. When using old or outdated pro-

ﬁles, the focus should be on common features as they

are less likely to have changed since the initial proﬁle

was collected.

Through inter-user PCA, features that differenti-

ate between users were found to be the DD and UD

digraphs of “5+R”, “l+Return”, “.+t”, “e+5”, “R+o”,

and “n+l”. The majority of these features also change

over time which explains the decrease in performance

over time when no updating strategy is used. While

digraphs were the most informative features, mono-

graphs were shown to still be useful in distinguish-

ing between users. This is consistent with previous

work by Ayotte, et al, (Ayotte et al., 2019; Ayotte

et al., 2020) where they found with small amounts of

keystrokes monographs were informative features for

distinguishing between users. Since monographs are

stable over time and may be able to differentiate be-

tween users, this makes them potential candidates for

use with old or outdated proﬁles.

Future work includes investigating the effects of

typing patterns on other datasets and with different

ﬁxed-text such as the datasets collected by Giot, et

al, and Fierrez, et al (Giot et al., 2009; Fierrez et al.,

2010). Our work will also be extended to free-text

keystroke dynamics to determine if the phenomenon

is generalizable beyond ﬁxed-text. Analysis of typ-

ing patterns across multiple years could provide even

more evidence of our conclusions. Lastly, we would

like to extend this work to investigate other sources of

variability within typing patterns such as mood, time

of day, or keyboard.

ACKNOWLEDGEMENTS

This work is supported in part by the NSF CPS award

1646542, Clarkson Niklas Ignite Fellowship, and ma-

terial is based upon work supported by the Center for

Identiﬁcation Technology Research (CITeR) and the

NSF under Grants 1650503 and 1314792.

Study of Intra- and Inter-user Variance in Password Keystroke Dynamics

473

REFERENCES

Abdi, H. and Williams, L. J. (2010). Principal component

analysis. Wiley interdisciplinary reviews: computa-

tional statistics, 2(4):433–459.

Alsultan, A. and Warwick, K. (2013). Keystroke dynamics

authentication: a survey of free-text methods. Inter-

national Journal of Computer Science Issues (IJCSI),

10(4):1.

Ayotte, B., Banavar, M., Hou, D., and Schuckers, S.

(2020). Fast free-text authentication via instance-

based keystroke dynamics. IEEE Transactions on

Biometrics, Behavior, and Identity Science, 2(4):377–

387.

Ayotte, B., Banavar, M. K., Hou, D., and Schuckers, S.

(2019). Fast and accurate continuous user authenti-

cation by fusion of instance-based, free-text keystroke

dynamics. In International Conference of the Biomet-

rics Special Interest Group (BIOSIG). IEEE.

Banerjee, S. and Woodard, D. (2012). Biometric authen-

tication and identiﬁcation using keystroke dynamics:

A survey. Journal of Pattern Recognition Research,

7(1):116–139.

C¸ eker, H. and Upadhyaya, S. (2017). Transfer learning in

long-text keystroke dynamics. In 2017 IEEE Interna-

tional Conference on Identity, Security and Behavior

Analysis (ISBA), pages 1–6. IEEE.

Duda, R. O., Hart, P. E., and Stork, D. G. (2012). Pattern

classiﬁcation. John Wiley & Sons.

Fierrez, J., Galbally, J., Ortega-Garcia, J., Freire, M. R.,

Alonso-Fernandez, F., Ramos, D., Toledano, D. T.,

Gonzalez-Rodriguez, J., Siguenza, J. A., Garrido-

Salas, J., et al. (2010). Biosecurid: a multimodal bio-

metric database. Pattern Analysis and Applications,

13(2):235–246.

Giot, R., Dorizzi, B., and Rosenberger, C. (2011). Anal-

ysis of template update strategies for keystroke dy-

namics. In 2011 IEEE Workshop on Computational

Intelligence in Biometrics and Identity Management

(CIBIM), pages 21–28. IEEE.

Giot, R., Dorizzi, B., and Rosenberger, C. (2015). A review

on the public benchmark databases for static keystroke

dynamics. Computers & Security, 55:46–61.

Giot, R., El-Abed, M., and Rosenberger, C. (2009). Gr-

eyc keystroke: a benchmark for keystroke dynamics

biometric systems. In 2009 IEEE 3rd International

Conference on Biometrics: Theory, Applications, and

Systems, pages 1–6. IEEE.

Gunetti, D. and Picardi, C. (2005). Keystroke analysis of

free text. ACM Trans. Inf. Syst. Secur., 8(3):312–347.

Gunetti, D., Picardi, C., and Ruffo, G. (2005). Dealing with

different languages and old proﬁles in keystroke anal-

ysis of free text. In Congress of the Italian Association

for Artiﬁcial Intelligence, pages 347–358. Springer.

Huang, J., Hou, D., Schuckers, S., Law, T., and Sherwin, A.

(2017). Benchmarking keystroke authentication algo-

rithms. In Information Forensics and Security (WIFS),

2017 IEEE Workshop on, pages 1–6. IEEE.

Kang, P., Hwang, S.-s., and Cho, S. (2007). Continual re-

training of keystroke dynamics based authenticator. In

International Conference on Biometrics, pages 1203–

1211. Springer.

Killourhy, K. S. and Maxion, R. A. (2009). Comparing

anomaly-detection algorithms for keystroke dynam-

ics. In 2009 IEEE/IFIP International Conference

on Dependable Systems & Networks, pages 125–134.

IEEE.

Mhenni, A., Cherrier, E., Rosenberger, C., and Amara, N.

E. B. (2019). Analysis of doddington zoo classiﬁca-

tion for user dependent template update: Application

to keystroke dynamics recognition. Future Generation

Computer Systems, 97:210–218.

Monrose, F. and Rubin, A. (1997). Authentication via

keystroke dynamics. In Proceedings of the 4th ACM

conference on Computer and communications secu-

rity, pages 48–56.

Ngugi, B., Kahn, B. K., and Tremaine, M. (2011). Typ-

ing biometrics: impact of human learning on perfor-

mance quality. Journal of Data and Information Qual-

ity (JDIQ), 2(2):11.

Teh, P. S., Teoh, A. B. J., and Yue, S. (2013). A survey of

keystroke dynamics biometrics. The Scientiﬁc World

Journal, 2013.

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